1. 浙江农林大学环境与资源学院, 碳中和学院,杭州,311300
2. 大连市生态环境事务服务中心,辽宁,大连,116000
3. 生态环境部南京环境科学研究所固体废物污染防治研究中心,南京,210042
4. 南京信息工程大学江苏省大气环境与装备技术协同创新中心,南京,210044
[ "范振浩(1998-),男,硕士研究生,主要从事土壤碳汇与全球气候变化研究。E-mail:zafu_fzh@163.com" ]
纸质出版:2023
移动端阅览
范振浩, 邢巍巍, 卜元卿, 等. 江苏省种植业碳排放的测算及达峰分析[J]. 水土保持学报, 2023,37(5):78-85.
FAN Zhenhao, XING Weiwei, BU Yuanqing, et al. Calculation and Peak Analysis of Carbon Emissions from Agricultural Planting in Jiangsu Province[J]. 2023, 37(5): 78-85.
范振浩, 邢巍巍, 卜元卿, 等. 江苏省种植业碳排放的测算及达峰分析[J]. 水土保持学报, 2023,37(5):78-85. DOI: 10.13870/j.cnki.stbcxb.2023.05.010.
FAN Zhenhao, XING Weiwei, BU Yuanqing, et al. Calculation and Peak Analysis of Carbon Emissions from Agricultural Planting in Jiangsu Province[J]. 2023, 37(5): 78-85. DOI: 10.13870/j.cnki.stbcxb.2023.05.010.
在实现"碳达峰、碳中和"的目标背景下
明确农业温室气体排放现状
模拟预测峰值
为促进江苏省农业低碳减排提供科学依据。基于农业物资投入和农田土壤利用2类碳源
采用IPCC碳排放系数法和清单法综合测算江苏省1990-2020年间种植业碳排放量
运用Tapio脱钩模型对农业碳排放量与农业经济增长的脱钩关系进行分析
并根据灰色预测模型GM(1
1)对2021-2060年碳排放量进行预测。结果表明:(1)2020年江苏省种植业碳排放为1 999.53万t
1990-2020年间呈现先增加后降低再增加然后趋于平稳的趋势;种植业碳排放主要来源于农田土壤利用
农田土壤利用碳排放占比为77.73%~86.95%
农资投入碳排放占比为13.05%~22.27%;(2)化肥是农资投入碳排放源中最主要的排放源
其占比为69.15%~79.20%
其次是农药和农膜
农机、灌溉、翻耕占比均较低;(3)水稻是农田土壤利用排放源中最主要的排放源
其占农田土壤利用碳排放的79.76%~87.23%
其次是小麦和蔬菜
大豆、玉米、棉花占比均较低;(4)脱钩关系以弱脱钩和强脱钩为主
表明随着种植业生产总值的提高
农业种植碳排放呈缓慢增长或递减的趋势;(5)分别以1990-2020(近30年)和1999-2020(近20年)年农业种植碳排放量数据为样本
预测2021-2060年江苏省农业种植碳排放呈持续增长趋势
2060年仍未达峰;以2010-2020(近10年)年农业种植碳排放量数据为样本预测
2021-2060年江苏省农业种植碳排放呈持续降低趋势
种植业碳排放已于2020年达到峰值
表明近年来江苏省在低碳绿色农业发展已初见成效。
In the context of achieving the goal of "carbon peaking and carbon neutrality"
we should clarify the current situation of agricultural greenhouse gas emissions
simulate and predict the peak value
and provide a scientific basis for promoting low-carbon emission reduction of agriculture in Jiangsu Province. Based on the two types of carbon sources of agricultural material input and farmland soil utilization
the IPCC carbon emission coefficient method and inventory method were used to comprehensively calculate the carbon emissions of the planting industry in Jiangsu Province from 1990 to 2020. The Tapio decoupling model was used to analyze the decoupling relationship between agricultural carbon emissions and agricultural economic growth
and the carbon emissions from 2021 to 2060 were predicted based on the grey prediction model GM (1
1). The results showed that:(1) The carbon emissions from agricultural planting in Jiangsu Province reached 1 999.53 tons in 2020. From 1990 to 2020
it first increased
then decreased
then increased
and then tended to be stable. Carbon emissions from agricultural planting mainly came from farmland soil use
accounting for 77.73%~86.95% of carbon emissions
and 13.05%~22.27% of carbon emissions from agricultural inputs. (2) Chemical fertilizer was the most important source of carbon emissions from agricultural inputs
accounting for 69.15%~79.20%
followed by pesticides and agricultural film
and agricultural machinery
irrigation and tillage had relatively low proportion. (3) Rice was the main emission source of farmland soil use
accounting for 79.76%~87.23% of farmland soil use carbon emissions
followed by wheat and vegetables
while soybean
corn and cotton accounted for a relatively low proportion. (4) The decoupling relationship was dominated by weak decoupling and strong decoupling
indicating a slow growth or decreasing trend of the carbon emissions from agricultural planting with the increasing of gross domestic product of the planting industry. (5) Taking the carbon emissions data of agricultural planting from 1990 to 2020 (nearly 30 years) and 1999 to 2020 (nearly 20 years) as samples
it was predicted that the carbon emissions of agricultural planting in Jiangsu Province will continue to increase from 2021 to 2060
and will not reach the peak in 2060. Based on the carbon emissions data of agricultural planting from 2010 to 2020
the carbon emissions of agricultural planting in Jiangsu Province will continue to decrease from 2021 to 2060
and has reached its peak in 2020
indicating that the development of low-carbon green agriculture in Jiangsu Province has achieved initial results in recent years.
郎慧,肖诗顺,王艳.四川省农业碳排放与经济增长的脱钩效应分析[J].山东农业大学学报(社会科学版),2019,21(2):69-78.
郝小雨.黑龙江省30年来农田生态系统碳源/汇强度及碳足迹变化[J].黑龙江农业科学,2021(8):97-104.
程琳琳.中国农业碳生产率时空分异:机理与实证[D].武汉:华中农业大学,2018.
孙英.山东省农业碳排放时空特征及其影响因素分析[D].兰州:西北师范大学,2018.
马永喜,孙亚丽.碳减排约束下区域农业生产投入及其环境效应:基于价格内生局部均衡模型的模拟[J].湖南农业大学学报(社会科学版),2021,22(5):15-23.
闵继胜,胡浩.中国农业生产温室气体排放量的测算[J].中国人口·资源与环境,2012,22(7):21-27.
田云,吴海涛.产业结构视角下的中国粮食主产区农业碳排放公平性研究[J].农业技术经济,2020(1):45-55.
田云,尹忞昊.中国农业碳排放再测算:基本现状、动态演进及空间溢出效应[J].中国农村经济,2022(3):104-127.
郑炎辉,何艳虎,王金杰,等.基于脱钩理论的区域产业节水目标研究:以广东省为例[J].水资源与水工程学报,2021,32(4):38-44.
李波,张俊飚,李海鹏.中国农业碳排放时空特征及影响因素分解[J].中国人口·资源与环境,2011,21(8):80-86.
廖卫东,刘淼.西部地区农业碳排放的时空演变及EKC假说检验:基于西部大开发12省份动态面板数据模型的经验分析[J].世界农业,2020(6):62-70.
Han H B, Zhong Z Q, Guo Y, et al. Coupling and decoupling effects of agricultural carbon emissions in China and their driving factors[J].Environmental Science and Pollution Research,2018,25(25):25280-25293.
尹岩,郗凤明,邴龙飞,等.我国设施农业碳排放核算及碳减排路径[J].应用生态学报,2021,32(11):3856-3864.
张丽琼,何婷婷.1997-2018年中国农业碳排放的时空演进与脱钩效应:基于空间和分布动态法的实证研究[J].云南农业大学学报(社会科学版),2022,16(1):78-90.
韩梦瑶,刘卫东,谢漪甜,等.中国省域碳排放的区域差异及脱钩趋势演变[J].资源科学,2021,43(4):710-721.
吴昊玥,孟越,黄瀚蛟,等.中国耕地利用净碳汇与农业生产的时空耦合特征[J].水土保持学报,2022,36(5):360-368.
李阳,陈敏鹏.中国省域农业源非CO
2
温室气体排放的影响因素分析与峰值预测[J].环境科学学报,2021,41(12):5174-5189.
国家统计局农村社会经济统计司.中国农村统计年鉴[M].北京:中国统计出版社,1991.
江苏省统计局.江苏统计年鉴[M].北京:中国统计出版社,1991.
Stocker T. Climate change 2013, in the physical science basis:Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[M].Cambridge:WMO/UNEP,2014.
West T O, Marland G. A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture:Comparing tillage practices in the United States[J].Agriculture, Ecosystems and Environment,2002,91(1/2/3):217-232.
智静,高吉喜.中国城乡居民食品消费碳排放对比分析[J].地理科学进展,2009,28(3):429-434.
王宝义,张卫国.中国农业生态效率测度及时空差异研究[J].中国人口·资源与环境,2016,26(6):11-19.
赵荣钦,刘英,丁明磊,等.河南省农田生态系统碳源/汇研究[J].河南农业科学,2010,39(7):40-44.
伍芬琳,李琳,张海林,等.保护性耕作对农田生态系统净碳释放量的影响[J].生态学杂志,2007,26(12):2035-2039.
苏维瀚,宋文质,张桦,等.华北典型冬麦区农田氧化亚氮通量[J].环境化学,1992,11(2):26-32.
刘杨,刘鸿斌.山东省农业碳排放特征、影响因素及达峰分析[J].中国生态农业学报,2022,30(4):558-569.
黄国宏,陈冠雄,吴杰,等.东北典型旱作农田N
2
O和CH
4
排放通量研究[J].应用生态学报,1995,6(4):383-386.
邱炜红,刘金山,胡承孝,等.种植蔬菜地与裸地氧化亚氮排放差异比较研究[J].生态环境学报,2010,19(12):2982-2985.
Tapio P. Towards a theory of decoupling:Degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001[J].Transport Policy,2005,12(2):137-151.
赵宇.江苏省农业碳排放动态变化影响因素分析及趋势预测[J].中国农业资源与区划,2018,39(5):97-102.
旷爱萍,胡超.广西农业碳排放影响因素和趋势预测[J].西南林业大学学报,2020,4(2):5-13.
何慧爽,付帮杰.我国粮食主产区农业碳排放测度与减排压力研究[J].生态经济,2019,35(11):99-104.
新华社.中共中央国务院印发《关于全面深化农村改革加快推进农业现代化的若干意见》[J]. 中华人民共和国国务院公报,2014(3):5-12.
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